Preprints
https://doi.org/10.5194/essd-2023-183
https://doi.org/10.5194/essd-2023-183
04 Aug 2023
 | 04 Aug 2023
Status: a revised version of this preprint was accepted for the journal ESSD and is expected to appear here in due course.

GPS displacement dataset for study of elastic surface mass variations

Athina Peidou, Donald Argus, Felix Landerer, David Wiese, and Matthias Ellmer

Abstract. Quantification of uncertainty in surface mass change signals derived from GPS measurements poses challenges, especially when dealing with large datasets with continental or global coverage. This study recommends a method for preparing and processing GPS measurements for use in a future joint solution with GRACE(-FO)-like observations. We assess the structure and quantify the uncertainty of vertical land displacement derived from 3045 GPS stations distributed across the continental US. Monthly means of daily positions are available for 15 years. We list the required corrections to isolate surface mass signals in GPS estimates and screen the data using GRACE(-FO) as external validation. Evaluation of GPS timeseries is a critical step, which identifies a) corrections that were missed; b) sites that contain non-elastic signals (e.g., close to aquifers); and c) sites affected by background modelling errors (e.g., errors in the glacial isostatic model). Finally, we quantify uncertainty of GPS vertical land displacement (VLD) estimates through stochastic modeling and quantification of spatially correlated errors. Our aim is to assign weights to GPS estimates of VLD, which will be used in a joint solution with GRACE(-FO). We prescribe white, colored and spatially correlated noise. To quantify spatially correlated noise, we build on the common mode imaging approach adding a geophysical constraint (i.e., surface hydrology) to derive an error estimate for the surface mass signal. We study the uncertainty derived using each technique and find that three techniques exhibit an average noise level between 2–3 mm: white noise, flicker noise, and RMS of residuals about a seasonality and trend fit. Prescribing random walk noise increases the error level such that half of the stations have noise > 4 mm, which is systematic with the noise level derived through modeling of spatial correlated noise. The new data set is suitable for use in a future joint solution with GRACE(-FO)-like observations.

Athina Peidou, Donald Argus, Felix Landerer, David Wiese, and Matthias Ellmer

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-183', Anonymous Referee #1, 29 Aug 2023
    • AC1: 'Reply on RC1', Athina Peidou, 26 Oct 2023
  • RC2: 'Comment on essd-2023-183', Anonymous Referee #2, 04 Sep 2023
    • AC2: 'Reply on RC2', Athina Peidou, 26 Oct 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-183', Anonymous Referee #1, 29 Aug 2023
    • AC1: 'Reply on RC1', Athina Peidou, 26 Oct 2023
  • RC2: 'Comment on essd-2023-183', Anonymous Referee #2, 04 Sep 2023
    • AC2: 'Reply on RC2', Athina Peidou, 26 Oct 2023
Athina Peidou, Donald Argus, Felix Landerer, David Wiese, and Matthias Ellmer

Data sets

GPS displacements and uncertainties to study elastic surface mass variations in North America Athina Peidou, Donald Argus, Matthias Ellmer, Felix Landerer, David Wiese https://zenodo.org/record/8184285

Athina Peidou, Donald Argus, Felix Landerer, David Wiese, and Matthias Ellmer

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Short summary
This study recommends a framework for preparing and processing vertical land displacements derived from Global Positioning System (GPS) positioning, for future integration with Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GRACE-FO) measurements. We derive GPS estimates that only reflect surface mass signals and evaluate them against GRACE(-FO). We also quantify uncertainty of GPS vertical land displacement estimates using various uncertainty quantification methods.
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